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SARDNET: A Self-Organizing Feature Map For Sequences (1995)
Daniel L. James
and
Risto Miikkulainen
A self-organizing neural network for sequence classification called SARDNET is described and analyzed experimentally. SARDNET extends the Kohonen Feature Map architecture with activation retention and decay in order to create unique distributed response patterns for different sequences. SARDNET yields extremely dense yet descriptive representations of sequential input in very few training iterations. The network has proven successful on mapping arbitrary sequences of binary and real numbers, as well as phonemic representations of English words. Potential applications include isolated spoken word recognition and cognitive science models of sequence processing.
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Citation:
In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors,
Advances in Neural Information Processing Systems 7 (NIPS'94)
, 577--584, Denver, CO, 1995. Cambridge, MA: MIT Press.
Bibtex:
@inproceedings{james:nips95, title={SARDNET: A Self-Organizing Feature Map For Sequences}, author={Daniel L. James and Risto Miikkulainen}, booktitle={Advances in Neural Information Processing Systems 7 (NIPS'94)}, editor={G. Tesauro and D. S. Touretzky and T. K. Leen}, address={Denver, CO}, publisher={Cambridge, MA: MIT Press}, pages={577--584}, url="http://nn.cs.utexas.edu/?james:nips95", year={1995} }
People
Daniel L. James
Undergraduate Alumni
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Areas of Interest
Natural Language Processing (Cognitive)
Unsupervised Learning, Clustering, and Self-Organization
Applications